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R, & Schwartz, J H (1985) Principles of neural science (2nd ed) New York: Elsevier Kerick, S E, Douglass, L, & Hatfield, B D (2004) Cerebral cortical adaptations associated with visuomotor practice Medicine and Science in Sport and Exercise, 36, 118 129 Kerick, S E, Hatfield, B D, & Allender, L E (2006) Eventrelated cortical dynamics of soldiers during shooting as a function of varied task demand Manuscript submitted for publication Kerick, S E, Iso-Ahola, S E, & Hatfield, B D (2000) Psychological momentum in target shooting: Cortical, cognitiveaffective, and behavioral responses Journal of Sport and Exercise Psychology, 22, 1 20 Kerick, S E, McDowell, K, Hung, T, Santa Maria, D L, Spalding, T W, & Hatfield, B D (2001) The role of the left temporal region under the cognitive motor demands of shooting in skilled marksmen.

Biological Psychology, 58, 263 277 Kinsbourne, M (1982) Hemispheric specialization and the growth of human understanding American Psychologist, 37, 411 420 Klimesch, W (1996) Memory processes, brain oscillations, and EEG synchronization International Journal of Psychophysiology, 24, 61 100 Klimesch, W (1999) EEG and theta oscillations reflect cognitive and memory performance: A review and analysis Brain Research Reviews, 29, 169 195 Klimesch, W, Doppelmayr, M, Russegger, H, & Pachinger, T (1996) Theta band power in the human scalp EEG and the encoding of new information Neuroreport, 7, 1235 1240 Konttinen, N, Landers, D M, & Lyytinen, H (2000) Aiming routines and their electrocortical concomitants among competitive rifle shooters Scandinavian Journal of Medicine and Science Sports, 10, 169 177 Konttinen, N, & Lyytinen, H (1992) Physiology of preparation: Brain slow waves, heart rate, and respiration preceding trig-.

(Cannot Say) (Lie) (Validity) (Correction)

As you ll see, some actions trigger multiple events. For example, when you insert a new worksheet into a workbook, this action triggers three Application-level events:

25

A posterior odds ratio of exactly 1.0 would mean that the evidence from the posterior distribution supports both classi cations equally. That is, the combination of information from the data and the prior distributions does not favor one category over the other. A value greater than 1.0 indicates that the posterior distribution favors the positive classi cation, while a value less than 1.0 represents evidence against the positive classi cation (e.g., churn = true). The value of the posterior odds ratio may be interpreted as indicating roughly the proportion or ratio of evidence provided by the posterior distribution in favor of the positive classi cation against the negative classi cation. In our example, the posterior odds ratio for a new customer who belongs to both plans is P(I V |C)P(C) P(I V |C)P(C) = 0.0108 = 0.6467 0.0168

Minimizing this quantity is achieved when its gradient is zero, leading to the expression 2 2 r r1 rn 2DxT HT H 2DrT H 0, (8:48) which is, nally, Dx (HT H) 1 HT Dr: (8:49)

0, 90, 180, 270 45, 135, 225, 315

Many of the stress-moderating factors previously associated with emotional adjustment to illness are also associated with medical utilization patterns. These include social support, life context, and personality characteristics. For instance, among medical patients attending 43 family practices, those with lower confidante support and lower emotional support showed a significantly greater number of office visits and a greater number of total charges over a one-year period (Broadhead, Hehlbach, DeGruy, & Kaplan, 1989). Contextual factors such as elevated life stress predicted a greater frequency of medical visits, and this association was strongest among patients with personality traits characterizing a tendency toward somaticizing (Miranda, Perez-Stable, Munoz, Hargreaves, & Henke, 1991). This work provides evidence that external life stressors may interact with tendencies to be overly focused on illness concerns in predicting medical utilization. Thus, stress reduction interventions might be particularly cost-effective in reducing overutilization of outpatient medical services, especially among patients who (1) possess this personality characteristic and (2) are undergoing significant life stressors. This line of work underlines the notion that identifying psychological characteristics of medical outpatients within their life context may facilitate triaging to time-limited, focused psychological intervention to substantially contain health care expenditures.

10.3.1.2 ERM Rules and Guidelines It is beyond the scope of this chapter to try to describe all the possible details that a good CDM written in the ERM language should have. It is even beyond the scope of this chapter to list all of the rules governing what a valid CDM can or cannot do in this modeling language. We will limit the discussion here to the bare minimum: a few basic naming rules (and even less-restrictive naming guidelines) followed by a few basic rules that control how the constructs can be combined and con gured within a data model. Although the rules are stricter than the guidelines, both are actually merely suggestions rather than absolute prohibitions. All data modelers who work together should create their own list of rules and guidelines as they create their DMs. Then, they should try to conform to these rules as much as possible. This means we should consider the rami cations carefully whenever we add, modify, or delete anything from our list.